386 citations as recorded by crossref.

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2. FOCA: a new quality-controlled database of floods and catchment descriptors in Italy P. Claps et al. 10.5194/essd-16-1503-2024
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8. NAC2H: The North American Climate Change and Hydroclimatology Data Set R. Arsenault et al. 10.1029/2020WR027097
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13. The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins G. Tang et al. 10.1029/2022WR033767
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18. Catchment response to climatic variability: implications for root zone storage and streamflow predictions N. Tempel et al. 10.5194/hess-28-4577-2024
19. A hydrologic signature approach to analysing wildfire impacts on overland flow L. Bolotin & H. McMillan 10.1002/hyp.15215
20. Applying transfer learning techniques to enhance the accuracy of streamflow prediction produced by long Short-term memory networks with data integration Y. Khoshkalam et al. 10.1016/j.jhydrol.2023.129682
21. A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments J. Yang et al. 10.3389/feart.2025.1538235
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23. From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale? W. Zhi et al. 10.1021/acs.est.0c06783
24. Achieving water budget closure through physical hydrological process modelling: insights from a large-sample study X. Zheng et al. 10.5194/hess-29-627-2025
25. Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization D. Jarajapu et al. 10.1016/j.jhydrol.2022.128341
26. airGRteaching: an open-source tool for teaching hydrological modeling with R O. Delaigue et al. 10.5194/hess-27-3293-2023
27. On understanding mountainous carbonate basins of the Mediterranean using parsimonious modeling solutions S. Azimi et al. 10.5194/hess-27-4485-2023
28. A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments P. Yeste et al. 10.5194/hess-28-5331-2024
29. A decomposition approach to evaluating the local performance of global streamflow reanalysis T. Zhao et al. 10.5194/hess-28-3597-2024
30. Improved Regionalization of the CN Method for Extreme Events at Ungauged Sites across the US T. Neelam et al. 10.1061/JHYEFF.HEENG-6180
31. A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022) X. Chen et al. 10.5194/essd-15-4463-2023
32. Can We Use the Water Budget to Infer Upland Catchment Behavior? The Role of Data Set Error Estimation and Interbasin Groundwater Flow B. Gordon et al. 10.1029/2021WR030966
33. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset C. Alvarez-Garreton et al. 10.5194/hess-22-5817-2018
34. To What Extent Are Changes in Flood Magnitude Related to Changes in Precipitation Extremes? H. Do et al. 10.1029/2020GL088684
35. Impact of spatial distribution information of rainfall in runoff simulation using deep learning method Y. Wang & H. Karimi 10.5194/hess-26-2387-2022
36. A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
37. Evaluating stochastic rainfall models for hydrological modelling T. Nguyen et al. 10.1016/j.jhydrol.2023.130381
38. rabpro: global watershed boundaries, river elevation profiles, and catchment statistics J. Schwenk et al. 10.21105/joss.04237
39. CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. 10.5194/essd-12-2459-2020
40. LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe C. Klingler et al. 10.5194/essd-13-4529-2021
41. Introductory overview: Recommendations for approaching scientific visualization with large environmental datasets C. Kelleher & A. Braswell 10.1016/j.envsoft.2021.105113
42. A Hydrologic Functional Approach for Improving Large‐Sample Hydrology Performance in Poorly Gauged Regions J. Janssen & A. Ameli 10.1029/2021WR030263
43. Gauging ungauged catchments – Active learning for the timing of point discharge observations in combination with continuous water level measurements S. Pool & J. Seibert 10.1016/j.jhydrol.2021.126448
44. Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data K. Tayal et al. 10.1088/1748-9326/ad6fb7
45. Catchment characterization: Current descriptors, knowledge gaps and future opportunities L. Tarasova et al. 10.1016/j.earscirev.2024.104739
46. BCUB – a large-sample ungauged basin attribute dataset for British Columbia, Canada D. Kovacek & S. Weijs 10.5194/essd-17-259-2025
47. A dataset of lake-catchment characteristics for the Tibetan Plateau J. Liu et al. 10.5194/essd-14-3791-2022
48. The Treatment of Uncertainty in Hydrometric Observations: A Probabilistic Description of Streamflow Records D. de Oliveira & J. Vrugt 10.1029/2022WR032263
49. Generalized Relationship Linking Water Balance and Vegetation Productivity across Site-to-Regional Scales G. Abeshu et al. 10.1061/JHYEFF.HEENG-6163
50. Location, location, location – Considering relative catchment location to understand subsurface losses M. Kiraz-Safari et al. 10.1016/j.jhydrol.2024.132328
51. CAMELS-DE: hydro-meteorological time series and attributes for 1582 catchments in Germany R. Loritz et al. 10.5194/essd-16-5625-2024
52. Time to Update the Split‐Sample Approach in Hydrological Model Calibration H. Shen et al. 10.1029/2021WR031523
53. A differentiable, physics-based hydrological model and its evaluation for data-limited basins W. Ouyang et al. 10.1016/j.jhydrol.2024.132471
54. Global patterns in vegetation accessible subsurface water storage emerge from spatially varying importance of individual drivers F. van Oorschot et al. 10.1088/1748-9326/ad8805
55. Advancing traditional strategies for testing hydrological model fitness in a changing climate A. Todorović et al. 10.1080/02626667.2022.2104646
56. Train, Inform, Borrow, or Combine? Approaches to Process‐Guided Deep Learning for Groundwater‐Influenced Stream Temperature Prediction J. Barclay et al. 10.1029/2023WR035327
57. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed T. Xu et al. 10.1029/2021WR030993
58. How is Baseflow Index (BFI) impacted by water resource management practices? J. Bloomfield et al. 10.5194/hess-25-5355-2021
59. Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations W. Knoben et al. 10.5194/gmd-12-2463-2019
60. Ensemble learning of catchment-wise optimized LSTMs enhances regional rainfall-runoff modelling − case Study: Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132269
61. Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model H. Yin et al. 10.1016/j.jhydrol.2021.126378
62. Spatial Delimitation of Small Headwater Catchments and Their Classification in Terms of Runoff Risks P. Kavka 10.3390/w13233458
63. Time Series Features for Supporting Hydrometeorological Explorations and Predictions in Ungauged Locations Using Large Datasets G. Papacharalampous & H. Tyralis 10.3390/w14101657
64. MacroSheds: A synthesis of long‐term biogeochemical, hydroclimatic, and geospatial data from small watershed ecosystem studies M. Vlah et al. 10.1002/lol2.10325
65. Comprehensive assessment of baseflow responses to long-term meteorological droughts across the United States S. Lee & H. Ajami 10.1016/j.jhydrol.2023.130256
66. Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models R. Arsenault et al. 10.5194/hess-27-139-2023
67. Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States E. Towler et al. 10.5194/hess-27-1809-2023
68. Future streamflow regime changes in the United States: assessment using functional classification M. Brunner et al. 10.5194/hess-24-3951-2020
69. Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E) J. Mai et al. 10.1061/(ASCE)HE.1943-5584.0002097
70. Advancing flood warning procedures in ungauged basins with machine learning Z. Rasheed et al. 10.1016/j.jhydrol.2022.127736
71. Hyperparameter optimization of regional hydrological LSTMs by random search: A case study from Basque Country, Spain F. Hosseini et al. 10.1016/j.jhydrol.2024.132003
72. The persistence of snow on the ground affects the shape of streamflow hydrographs over space and time: a continental-scale analysis E. Le et al. 10.3389/fenvs.2023.1207508
73. Mapping (dis)agreement in hydrologic projections L. Melsen et al. 10.5194/hess-22-1775-2018
74. Uncertainties in measuring and estimating water‐budget components: Current state of the science S. Levin et al. 10.1002/wat2.1646
75. Dual-Stage Attention-Based LSTM Network for Multiple Time Steps Flood Forecasting F. Wang et al. 10.5194/piahs-386-141-2024
76. Evaluation of random forests and Prophet for daily streamflow forecasting G. Papacharalampous & H. Tyralis 10.5194/adgeo-45-201-2018
77. Niederschlags-Abfluss-Modellierung mit Long Short-Term Memory (LSTM) F. Kratzert et al. 10.1007/s00506-021-00767-z
78. Beyond Counting Zeroes: Using Entropy-Based Hydrologic Signatures and Classification for Streamflow Intermittency Assessment I. Niadas & C. Makropoulos 10.1007/s11269-024-03881-1
79. Adaptive Reservoir Inflow Forecasting Using Variational Mode Decomposition and Long Short-Term Memory H. Hu et al. 10.1109/ACCESS.2021.3107502
80. HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. 10.5194/hess-26-4773-2022
81. Streamflow-based evaluation of climate model sub-selection methods J. Kiesel et al. 10.1007/s10584-020-02854-8
82. Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections K. Fowler et al. 10.1029/2019WR025286
83. Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study S. Pool et al. 10.1029/2021WR030437
84. Runoff for Russia (RFR v1.0): The Large-Sample Dataset of Simulated Runoff and Its Characteristics G. Ayzel 10.3390/data8020031
85. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al. 10.3390/w16131904
86. Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US G. Konapala et al. 10.1088/1748-9326/aba927
87. Correspondence Between Model Structures and Hydrological Signatures: A Large‐Sample Case Study Using 508 Brazilian Catchments P. David et al. 10.1029/2021WR030619
88. QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany P. Ebeling et al. 10.5194/essd-14-3715-2022
89. Global patterns in water flux partitioning: Irrigated and rainfed agriculture drives asymmetrical flux to vegetation over runoff D. Althoff & G. Destouni 10.1016/j.oneear.2023.08.002
90. Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
91. CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil V. Chagas et al. 10.5194/essd-12-2075-2020
92. Impacts of agriculture and snow dynamics on catchment water balance in the U.S. and Great Britain M. Zaerpour et al. 10.1038/s43247-024-01891-w
93. Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
94. Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
95. Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs S. Bhanja et al. 10.1016/j.jhydrol.2023.129176
96. Hybrid forecasting: blending climate predictions with AI models L. Slater et al. 10.5194/hess-27-1865-2023
97. Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
98. Interactions between snow cover and evaporation lead to higher sensitivity of streamflow to temperature A. Meira Neto et al. 10.1038/s43247-020-00056-9
99. Modeling hydraulic heads with impulse response functions in different environmental settings of the Baltic countries M. Jemeļjanova et al. 10.1016/j.ejrh.2023.101416
100. Toward interpretable LSTM-based modeling of hydrological systems L. De la Fuente et al. 10.5194/hess-28-945-2024
101. Flood Frequency Modeling and Prediction of Beki and Pagladia Rivers Using Deep Learning Approach G. Devi et al. 10.1007/s11063-022-10773-1
102. Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032 T. van Hateren et al. 10.1080/02626667.2023.2170754
103. Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction T. Xie et al. 10.3390/w16010069
104. A synthesis of Global Streamflow Characteristics, Hydrometeorology, and Catchment Attributes (GSHA) for large sample river-centric studies Z. Yin et al. 10.5194/essd-16-1559-2024
105. On the challenges of global entity-aware deep learning models for groundwater level prediction B. Heudorfer et al. 10.5194/hess-28-525-2024
106. DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani 10.1038/s41597-025-04389-y
107. Higher-order internal modes of variability imprinted in year-to-year California streamflow changes S. Duan et al. 10.1038/s43247-024-01594-2
108. VMDI-LSTM-ED: A novel enhanced decomposition ensemble model incorporating data integration for accurate non-stationary daily streamflow forecasting J. Liu et al. 10.1016/j.jhydrol.2025.132769
109. Comparison of eight filter-based feature selection methods for monthly streamflow forecasting – Three case studies on CAMELS data sets K. Ren et al. 10.1016/j.jhydrol.2020.124897
110. Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network C. Frank et al. 10.3389/frwa.2023.1126310
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112. Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach G. Konapala et al. 10.1029/2020WR027340
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114. Hydrological signatures in Brazilian catchments: a machine learning approach for spatial estimates G. de Oliveira Corrêa et al. 10.1080/02626667.2024.2446265
115. Use of streamflow indices to identify the catchment drivers of hydrographs J. Mathai & P. Mujumdar 10.5194/hess-26-2019-2022
116. A Novel Approach for High-Performance Estimation of SPI Data in Drought Prediction L. Latifoğlu & M. Özger 10.3390/su151914046
117. Climate and rivers G. McGregor 10.1002/rra.3508
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119. Runoff Prediction Based on Deep Residual Shrinkage Long Short-term Memory Network Z. Guan et al. 10.1088/1742-6596/2400/1/012016
120. Can transfer learning improve hydrological predictions in the alpine regions? Y. Yao et al. 10.1016/j.jhydrol.2023.130038
121. Comparison of neural network, Gaussian regression, support vector machine, long short-term memory, multi-gene genetic programming, and M5 Trees methods for solving civil engineering problems E. Uncuoglu et al. 10.1016/j.asoc.2022.109623
122. Hydrograph and recession flows simulations using deep learning: Watershed uniqueness and objective functions A. Gupta & S. McKenna 10.1016/j.hydroa.2024.100198
123. Catchment processes can amplify the effect of increasing rainfall variability M. Müller et al. 10.1088/1748-9326/ac153e
124. Assessment of monthly runoff simulations based on a physics-informed machine learning framework: The effect of intermediate variables in its construction C. Deng et al. 10.1016/j.jenvman.2024.121299
125. Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks A. Sun et al. 10.1029/2021WR030394
126. On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. 10.5194/hess-28-5011-2024
127. Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M. Gauch et al. 10.5194/hess-25-2045-2021
128. Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs Y. Shen et al. 10.5194/essd-15-2781-2023
129. Dissolving the mystery of subsurface controls on snowmelt–discharge dynamics in karst mountain watersheds using hydrologic timeseries D. Thurber et al. 10.1002/hyp.15170
130. Post‐Processing the National Water Model with Long Short‐Term Memory Networks for Streamflow Predictions and Model Diagnostics J. Frame et al. 10.1111/1752-1688.12964
131. Solute export patterns across the contiguous USA D. Kincaid et al. 10.1002/hyp.15197
132. An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation Y. Sawada 10.1029/2021MS002882
133. Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
134. Theoretical and empirical evidence against the Budyko catchment trajectory conjecture N. Reaver et al. 10.5194/hess-26-1507-2022
135. ML4FF: A machine-learning framework for flash flood forecasting applied to a Brazilian watershed J. Soares et al. 10.1016/j.jhydrol.2025.132674
136. Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S) D. Hernandez et al. 10.1029/2021WR031860
137. Learning landscape features from streamflow with autoencoders A. Bassi et al. 10.5194/hess-28-4971-2024
138. Improving AI System Awareness of Geoscience Knowledge: Symbiotic Integration of Physical Approaches and Deep Learning S. Jiang et al. 10.1029/2020GL088229
139. A unified framework of water balance models for monthly, annual, and mean annual timescales X. Zhang et al. 10.1016/j.jhydrol.2020.125186
140. Uncertainty estimation with deep learning for rainfall–runoff modeling D. Klotz et al. 10.5194/hess-26-1673-2022
141. Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
142. A Novel Strategy for Automatic Selection of Cross‐Basin Data to Improve Local Machine Learning‐Based Runoff Models C. Nai et al. 10.1029/2023WR035051
143. Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization S. Ragettli et al. 10.1016/j.jhydrol.2017.10.031
144. A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves C. Leong & Y. Yokoo 10.1002/hyp.14569
145. The proper care and feeding of CAMELS: How limited training data affects streamflow prediction M. Gauch et al. 10.1016/j.envsoft.2020.104926
146. A hybrid Budyko-type regression framework for estimating baseflow from climate and catchment attributes S. Chen & X. Ruan 10.1016/j.jhydrol.2023.129118
147. Probabilistic Hydrological Post-Processing at Scale: Why and How to Apply Machine-Learning Quantile Regression Algorithms G. Papacharalampous et al. 10.3390/w11102126
148. Multi-step ahead forecasting of daily streamflow based on the transform-based deep learning model under different scenarios M. He et al. 10.1038/s41598-025-89837-w
149. A new criteria for determining the best decomposition level and filter for wavelet-based data-driven forecasting frameworks- validating using three case studies on the CAMELS dataset M. Mazarei Behbahani & A. Mazarei 10.1007/s00477-023-02531-z
150. Direct vegetation response to recent CO2 rise shows limited effect on global streamflow H. Wei et al. 10.1038/s41467-024-53879-x
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152. Vegetation patterns governing the competitive relationship between runoff and evapotranspiration using a novel water balance model at a semi-arid watershed Q. Fang et al. 10.1016/j.envres.2022.113976
153. Hydroclimatic time series features at multiple time scales G. Papacharalampous et al. 10.1016/j.jhydrol.2023.129160
154. Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow H. Tyralis & G. Papacharalampous 10.5194/adgeo-45-147-2018
155. National-scale geodatabase of catchment characteristics in the Philippines for river management applications R. Boothroyd et al. 10.1371/journal.pone.0281933
156. Transferring Hydrologic Data Across Continents – Leveraging Data‐Rich Regions to Improve Hydrologic Prediction in Data‐Sparse Regions K. Ma et al. 10.1029/2020WR028600
157. Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States O. Rakovec et al. 10.1029/2019JD030767
158. TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
159. Catchment natural driving factors and prediction of baseflow index for Continental United States based on Random Forest technique S. Huang et al. 10.1007/s00477-021-02057-2
160. Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships K. Xie et al. 10.1016/j.jhydrol.2021.127043
161. Quantifying the Geomorphic Effect of Floods Using Satellite Observations of River Mobility A. Leenman et al. 10.1029/2023GL103875
162. Seasonality and Drivers of Low Flows Across Europe and the United States M. Floriancic et al. 10.1029/2019WR026928
163. Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data S. Goeking & D. Tarboton 10.3390/w14152414
164. Runoff predictions in new-gauged basins using two transformer-based models H. Yin et al. 10.1016/j.jhydrol.2023.129684
165. What control the spatial patterns and predictions of runoff response over the contiguous USA? S. Jiang et al. 10.1007/s11442-024-2249-4
166. Technical note: Do different projections matter for the Budyko framework? R. Nijzink & S. Schymanski 10.5194/hess-26-4575-2022
167. Variational Bayesian dropout with a Gaussian prior for recurrent neural networks application in rainfall–runoff modeling S. Sadeghi Tabas & S. Samadi 10.1088/1748-9326/ac7247
168. The importance of source data in river network connectivity modeling: A review C. Brinkerhoff 10.1002/lno.12706
169. What is the hydrologically effective area of a catchment? Y. Liu et al. 10.1088/1748-9326/aba7e5
170. The role of previously glaciated landscapes in spatiotemporal variability of streamflow in snow-dominated watersheds: British Columbia, Canada X. Chen et al. 10.1016/j.ejrh.2023.101478
171. Horton Index: Conceptual Framework for Exploring Multi‐Scale Links Between Catchment Water Balance and Vegetation Dynamics G. Abeshu & H. Li 10.1029/2020WR029343
172. Technical Note: The divide and measure nonconformity – how metrics can mislead when we evaluate on different data partitions D. Klotz et al. 10.5194/hess-28-3665-2024
173. Enhancing Predictions in Ungauged Basins Using Machine Learning to Its Full Potential T. Fadziso 10.18034/ajase.v8i1.10
174. Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems U. Ehret & P. Dey 10.5194/hess-27-2591-2023
175. Variable Streamflow Response to Forest Disturbance in the Western US: A Large‐Sample Hydrology Approach S. Goeking & D. Tarboton 10.1029/2021WR031575
176. AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods A. Abbas et al. 10.5194/gmd-15-3021-2022
177. Machine‐Learning Reveals Equifinality in Drivers of Stream DOC Concentration at Continental Scales K. Underwood et al. 10.1029/2021WR030551
178. Information and disinformation in hydrological data across space: The case of streamflow predictions using machine learning A. Gupta 10.1016/j.ejrh.2023.101607
179. Climate Controls on River Chemistry L. Li et al. 10.1029/2021EF002603
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266. Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality? C. Varadharajan et al. 10.1002/hyp.14565
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271. PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
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281. Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters L. Bouaziz et al. 10.5194/hess-26-1295-2022
282. Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges E. Ragno et al. 10.5194/hess-26-1695-2022
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293. Exploring the provenance of information across Canadian hydrometric stations: implications for discharge estimation and uncertainty quantification S. Gharari et al. 10.5194/hess-28-4383-2024
294. Improving the Representation of Subsurface Water Movement in Land Models S. Gharari et al. 10.1175/JHM-D-19-0108.1
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302. Direct and Indirect Drivers of Energy and Nutrient Availability in Freshwater Ecosystems Across Spatial Scales H. Fazekas et al. 10.1029/2022GB007580
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312. Historical and future changes in global flood magnitude – evidence from a model–observation investigation H. Do et al. 10.5194/hess-24-1543-2020
313. Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning F. Kratzert et al. 10.1029/2019WR026065
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343. How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset H. Tyralis et al. 10.1016/j.jhydrol.2019.04.070
344. An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting W. Fang et al. 10.1038/s41598-024-81502-y
345. Deep learning rainfall–runoff predictions of extreme events J. Frame et al. 10.5194/hess-26-3377-2022
346. Earthquake Nowcasting with Deep Learning G. Fox et al. 10.3390/geohazards3020011
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349. Is There a Baseflow Budyko Curve? S. Gnann et al. 10.1029/2018WR024464
350. Benchmarking data-driven rainfall-runoff modeling across 54 catchments in the Yellow River Basin: Overfitting, calibration length, dry frequency J. Jin et al. 10.1016/j.ejrh.2022.101119
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352. Factors influencing calibration of a semi-distributed mixed runoff hydrological model: A study on nine small mountain catchments in China L. Wen et al. 10.1016/j.ejrh.2023.101418
353. Expectile-based hydrological modelling for uncertainty estimation: Life after mean H. Tyralis et al. 10.1016/j.jhydrol.2022.128986
354. A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling F. Kratzert et al. 10.5194/hess-25-2685-2021
355. Using Physics-Encoded GeoAI to Improve the Physical Realism of Deep Learning′s Rainfall-Runoff Responses under Climate Change H. Li et al. 10.1016/j.jag.2024.104101
356. RR-Former: Rainfall-runoff modeling based on Transformer H. Yin et al. 10.1016/j.jhydrol.2022.127781
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359. Simbi: historical hydro-meteorological time series and signatures for 24 catchments in Haiti R. Bathelemy et al. 10.5194/essd-16-2073-2024
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